class_docstr: Invertible `TextEncoder` using word pieces with a byte-level fallback.
  
  Encoding is fully invertible because all out-of-vocab wordpieces are
  byte-encoded.
  
  The vocabulary is "trained" on a corpus and all wordpieces are stored in a
  vocabulary file. To generate a vocabulary from a corpus, use
  `tfds.deprecated.text.SubwordTextEncoder.build_from_corpus`.
  
  Typical usage:
  
  ```
  # Build
  encoder = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(
      corpus_generator, target_vocab_size=2**15)
  encoder.save_to_file(vocab_fname)
  
  # Load
  encoder = tfds.deprecated.text.SubwordTextEncoder.load_from_file(vocab_fname)
  ids = encoder.encode("hello world")
  text = encoder.decode([1, 2, 3, 4])
  ```
docstr: Encode a single token byte-wise into integer ids.
docstr_tok: ["Encode","a","single","token","byte-wise","into","integer","ids","."]
code: def _byte_encode(self, token):
      """Encode a single token byte-wise into integer ids."""
      # Vocab ids for all bytes follow ids for the subwords
      offset = len(self._subwords)
      if token == "_":
          return [len(self._subwords) + ord(" ")]
      return [
          i + offset for i in list(bytearray(tf.compat.as_bytes(token)))
      ]
  
imports: from __future__ import unicode_literals
  import collections
  from absl import logging
  from tensorflow_datasets.core.deprecated.text import text_encoder
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 7 for calling the SubwordTextEncoder obj's underlying member method code with example usage: obj._byte_encode(token) and returns: unknown, list